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First, the correlation between sets of p-values was calculated using Spearman's rho.
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The correlation between the two sets of p-values was almost perfect (r = 0.997).
This approach can be useful to select a rejection level that balances the number of false positives and negatives and to assess the degree of overlap between the two sets of P-values.
For each setting, 10 000 datasets were generated, yielding 10 000 sets of P-values from which the correlation between P-values could be estimated.
Nevertheless, both sets of p-values were substantively no different.
We applied MACRO-APE to estimate the similarities between the models for a set of P-values each time specifying the same P-value for both compared PWMs.
The overlap between sets of SNPs in the bottom 5% of p-values based on the new test and the test without PS correction was about 80%, with the majority of discordant SNPs having both ranks close to the threshold.
To assess uniqueness, we set a threshold on the ratio of p-values between the best solution and the next-best solution (default of five).
Statistical significance of gene-set scores between the different synovial phenotypes was calculated using the t-test followed by Benjamini-Hochberg correction of P-values [ 29].
An unpaired, two-tailed Student's t-test was performed to determine the significance of differences between the data sets and P-values were considered significant at the 95% confidence interval (CI).
The graph shows both the ratio between both sets of genes and p-values associated to their differential response to each stress.
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